
import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration




def       _SCREAMING_SNAKE_CASE   (						_lowerCamelCase					:     int		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     str		,    _lowerCamelCase					:     Path		,    _lowerCamelCase					:     str = None		,    _lowerCamelCase					:     str = None		,    _lowerCamelCase					:     str = None		,    )   ->       Any:






							'''simple docstring'''





							if config_name_or_path is None:
														__UpperCamelCase      :  Dict       =   "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

							if generator_tokenizer_name_or_path is None:
														__UpperCamelCase      :  Any       =   generator_name_or_path

							if question_encoder_tokenizer_name_or_path is None:
														__UpperCamelCase      :  Union[str, Any]       =   question_encoder_name_or_path

							__UpperCamelCase      :  Tuple       =   RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

							# Save model.
							__UpperCamelCase      :  int       =   RagConfig.from_pretrained(_lowerCamelCase)
							__UpperCamelCase      :  List[Any]       =   AutoConfig.from_pretrained(_lowerCamelCase)
							__UpperCamelCase      :  List[str]       =   AutoConfig.from_pretrained(_lowerCamelCase)

							__UpperCamelCase      :  int       =   gen_config
							__UpperCamelCase      :  Tuple       =   question_encoder_config

							__UpperCamelCase      :  Optional[Any]       =   model_class.from_pretrained_question_encoder_generator(
							    _lowerCamelCase		,    _lowerCamelCase		,    config=_lowerCamelCase)
							rag_model.save_pretrained(_lowerCamelCase)

							# Sanity check.
							model_class.from_pretrained(_lowerCamelCase)

							# Save tokenizers.
							__UpperCamelCase      :  Tuple       =   AutoTokenizer.from_pretrained(_lowerCamelCase)
							gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/")
							__UpperCamelCase      :  Union[str, Any]       =   AutoTokenizer.from_pretrained(_lowerCamelCase)
							question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/")


if __name__ == "__main__":
		lowercase    :   List[Any]						=			argparse.ArgumentParser()
		parser.add_argument(
		    '--model_type',
		    choices=['rag_sequence', 'rag_token'],
		    required=True,
		    type=str,
		    help='RAG model type: rag_sequence, rag_token',
		)
		parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
		parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
		parser.add_argument(
		    '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
		)

		parser.add_argument(
		    '--generator_tokenizer_name_or_path',
		    type=str,
		    help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
		)
		parser.add_argument(
		    '--question_encoder_tokenizer_name_or_path',
		    type=str,
		    help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
		)
		parser.add_argument(
		    '--config_name_or_path',
		    type=str,
		    help=(
		        'Identifier of the model config to use, if not provided, resolves to a base config for a given'
		        ' ``model_type``'
		    ),
		)

		lowercase    :   Dict						=			parser.parse_args()

		lowercase    :   str						=			Path(args.dest)
		dest_dir.mkdir(exist_ok=True)

		consolidate(
		    args.model_type,
		    args.generator_name_or_path,
		    args.question_encoder_name_or_path,
		    dest_dir,
		    args.config_name_or_path,
		    args.generator_tokenizer_name_or_path,
		    args.question_encoder_tokenizer_name_or_path,
		)

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase    :   Tuple						=			{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase    :   int						=			[
		    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'LukeForEntityClassification',
		    'LukeForEntityPairClassification',
		    'LukeForEntitySpanClassification',
		    'LukeForMultipleChoice',
		    'LukeForQuestionAnswering',
		    'LukeForSequenceClassification',
		    'LukeForTokenClassification',
		    'LukeForMaskedLM',
		    'LukeModel',
		    'LukePreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
		from .tokenization_luke import LukeTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_luke import (
				    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LukeForEntityClassification,
				    LukeForEntityPairClassification,
				    LukeForEntitySpanClassification,
				    LukeForMaskedLM,
				    LukeForMultipleChoice,
				    LukeForQuestionAnswering,
				    LukeForSequenceClassification,
				    LukeForTokenClassification,
				    LukeModel,
				    LukePreTrainedModel,
				)

else:
		import sys

		lowercase    :   Any						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)